As computational systems become increasingly more complex, the data output has become more varied and complex. In particular, for system data output for diagnostic purposes from a mass data storage system, this is especially true. It is not uncommon for such diagnostic data captured as "log data" in "system log" files to be:
(1.1) extremely voluminous: In fact, such logs can be some of the system's largest data storage collections; PA1 (1.2) extremely cryptic: It is not uncommon for the data to be encoded as merely strings of bits. Further, the data can be in a plurality of substantially distinct data formats represented among the bit strings where each format has a unique set of interpretation rules. Note that this can be particularly true of computational systems with cooperating devices where the individual devices were developed substantially in isolation from one another; PA1 (1.3) subject to frequent change: The format and information content of log data records may fluctuate substantially as new information is required for new computational system problems that must be diagnosed. PA1 (1.4) irrelevant data: Large amounts of data may be useless for diagnosis; e.g., because it is irrelevant or outdated and thus no longer applicable.
Furthermore, since the computational system anomalies may be so intermittent or sparsely manifested in the log that they are difficult to detect. Thus, it is not unusual for there to be only a few individuals who possess a sufficient background to understand such log data. Moreover, these individuals are likely to be senior engineers whose time is in high demand as system designers rather than system debuggers. To facilitate the use of such diagnostic data, a data translation and retrieval system is required which substantially alleviates the burden of the above drawbacks, (1.1) through (1.4). By translating between the cryptic ideocycratic formats within such data and a more uniform and comprehensible data representation, a more cost effective diagnosis process can be instituted whereby fewer highly skilled personnel are required in the diagnosis of, for example, computational system faults at a customer site.